Xiaodong Cao

18 papers B 1Journal 9Unranked 8
YearRankTypeTitle / Venue / Authors
2026 J jnl
Int. J. Hum. Comput. Interact.
Chenyuan Yang, Liping Pang, Xiaodong Cao, Peng Xue, Bingxu Zhao, Wentao Wu
2025 J jnl
Symmetry
Weifeng Liu, Wenchang Li, Xiaodong Cao, Yihao Fu, Juping Wu, Jian Liu, Aidong Chen, Yanlong Zhang, Shuo Wang, Jing Zhou
2024 conf
EITCE
Yong Yu, Shaoheng Zhong, Jinrong Chen, Xiaodong Cao
2023 J jnl
Sensors
Hongyan Zhang, Guiqing Xu, Yuming Chen, Xu Li, Shaopeng Wang, Feihao Jiang, Pengyang Zhan, Chuanfu Lu, Xiaodong Cao, Yongkang Ye, Yunlai Tao
2023 B conf
TrustCom
Runsha Dong, Xiaodong Cao, Chao Wang, Zhaoyang Sun, Lexi Xu, Xin He, Yang Wu
2021 conf
ICT-DM
Jinghui Li, Xiaodong Cao, Shengli Guo, Runsha Dong, Chuntao Song, Tianyi Wang, Zelin Wang
2021 conf
ICT-DM
Runsha Dong, Xiaodong Cao, Jinghui Li, Tianyi Wang, Chuntao Song, Shengli Guo, Lexi Xu, Xiaomeng Zhu, Chen Cheng
2021 conf
ICT-DM
Shengli Guo, Jinghui Li, Xiaodong Cao, Zelin Wang, Chuntao Song, Runsha Dong, Lexi Xu
2020 conf
ADHIP (1)
Shihai Yang, Xiaodong Cao, Weiguo Zhang, Feng Ji
2020 J jnl
Sensors
Xiaodong Cao, Christian Rembe
2020 conf
ADHIP (1)
Feng Ji, Shihai Yang, Xiaodong Cao, Yong-Biao Yang
2019 J jnl
IEEE Access
Shengyang Liu, Lei Dong, Xiaozhong Liao, Xiaodong Cao, Xiaoxiao Wang, Bo Wang
2019 J jnl
Sensors
Xiaodong Cao, Xueting Zhu, Shudong He, Xuan Xu, Yongkang Ye
2019 J jnl
Sensors
Shengyang Liu, Lei Dong, Xiaozhong Liao, Xiaodong Cao, Xiaoxiao Wang
2017 J jnl
J. Vis.
Jiayu Li, Junjie Liu, Congcong Wang, Nan Jiang, Xiaodong Cao
2016 conf
ISCIT
Jian Guan, Lijuan Cao, Weiwei Chen, Xinzhou Cheng, Lexi Xu, Xiaodong Cao
2015 J jnl
Appl. Math. Comput.
Xiaodong Cao, Xu You
2005 conf
ICCNMC
Yongjian Yang, Yajun Chen, Xiaodong Cao, Jiubin Ju
redb/extractors/decompiler/bninja/similarity/minhasher.py
← Index redb/extractors/decompiler/bninja/similarity/minhasher.py python
import logging
import random
from enum import Enum

from ..analysis.medium_level_normalization import MediumLevelNormalization

try:
    from .minhashcustom import MinHashCustom
    from ..analysis.low_level_normalization import LowLevelNormalization
except ImportError:
    # Fallback to absolute imports (for multiprocessing spawned processes)
    from redb.extractors.decompiler.bninja.similarity.minhashcustom import MinHashCustom
    from redb.extractors.decompiler.bninja.analysis.low_level_normalization import LowLevelNormalization

## Values for this configuration were extracted from https://github.com/danielplohmann/mcrit/blob/main/mcrit/config/MinHashConfig.py#L10
# Length in number of Shingles of which a minhash consists
# this value represents the length of sha256sum hash truncated
MINHASH_SIGNATURE_LENGTH: int = 64
# Number of bits per signature element (1-32 bits)
MINHASH_SIGNATURE_BITS: int = 8


class TokenKind(Enum):
    LLIL = "llil"
    TYPED_LLIL = "typed_llil"
    MLIL = "mlil"
    TYPED_MLIL = "typed_mlil"


class MinHasher:
    # stick to the default method
    MINHASH_STRATEGY_HASH_ALL = 1

    def __init__(self, seed, il_function, kind: TokenKind = TokenKind.LLIL):
        self._minhash_seeds = []
        self.il_func = il_function
        self.kind = kind
        self._minhash_permutation = []
        self._signature_segments = []
        self._initMinhashing(seed)

    def _initMinhashing(self, MINHASH_SEED=None):
        random.seed(MINHASH_SEED)
        # init sequence of seeds
        self._minhash_seeds = [
            random.randint(0, MinHashCustom.getHashMax()) for _ in range(MINHASH_SIGNATURE_LENGTH)
        ]

    def make_ngrams(self, tokens, n=3):
        """Take the ngrams of the IL we try to pass into the functions"""
        return [tuple(tokens[i:i+n]) for i in range(len(tokens) - n + 1)]

    def _extract_tokens(self):
        """Extract the IL tokens from the IL function, picking the right
        normalizer (LLIL/MLIL) and the right normalization mode
        (skeleton/typed) based on self.kind."""
        if self.kind in (TokenKind.LLIL, TokenKind.TYPED_LLIL):
            normalizer = LowLevelNormalization()
        elif self.kind in (TokenKind.MLIL, TokenKind.TYPED_MLIL):
            normalizer = MediumLevelNormalization()
        else:
            raise ValueError(f"Unsupported token kind: {self.kind}")

        # typed variants include operand type info, skeleton variants don't
        if self.kind in (TokenKind.TYPED_LLIL, TokenKind.TYPED_MLIL):
            normalize = normalizer.normalize_instr_with_operands
        else:
            normalize = normalizer.normalize_instruction_all_levels

        instructions = []
        for basic_block in self.il_func.basic_blocks:
            for il in basic_block:
                instructions.append(normalize(il))

        return instructions

    def calculateMinHash(self):
        """Calculate hash function every time, then take minimum shingle per shingler"""
        minhash_result = MinHashCustom(minhash_bits=MINHASH_SIGNATURE_BITS)
        minhash_signature = []

        tokens = self._extract_tokens()
        shingles = self.make_ngrams(tokens, n=3)

        # Functions with fewer than 3 IL instructions can't produce n-grams
        # Return empty minhash for such small functions (thunks, stubs, etc.)
        # Triggered by 39d8ad95b0323c37bd3134ab93ac4af44c66a1a8443a41c1ac02cec19bb2816a
        if not shingles:
            return []

        # Generate the MinHash
        for seed in self._minhash_seeds:
            hashed_shingles = [
                self.shingle_hash(shingle, seed) for shingle in shingles
            ]
            min_value = min(hashed_shingles)

            if MINHASH_SIGNATURE_BITS < 32:
                min_value %= (2 ** MINHASH_SIGNATURE_BITS)

            minhash_signature.append(min_value)

        minhash_result.setMinHash(minhash_signature)
        return minhash_result.getMinHashInt()

    def shingle_hash(self, shingle, hash_seed=0):
        """Produce a single 32bit UINT hash for a given shingle"""
        return MinHashCustom.hashData(shingle, hash_seed)